Algorithms used for decision-making in higher education promise cost-savings to institutions and personalized service for students, but at the same time, raise ethical challenges around surveillance, fairness, and interpretation of data. To address the lack of systematic understanding of how these algorithms are currently designed, we reviewed algorithms proposed by the research community for higher education. We explored the current trends in the use of computational methods, data types, and target outcomes, and analyzed the role of human-centered algorithm design approaches in their development. Our preliminary research suggests that the models are trending towards deep learning, increased use of student personal data and protected attributes, with the target scope expanding towards automated decisions. Despite the associated decrease in interpretability and explainability, current development predominantly fails to incorporate human-centered lenses.